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Bayesian networks in fault diagnosis...

Cai, Baoping.

Bayesian networks in fault diagnosis[electronic resource] :practice and application /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	519.542
書名/作者:	Bayesian networks in fault diagnosis : practice and application // editors, Baoping Cai ... [et al.]
其他作者:	Cai, Baoping.
出版者:	Singapore : : World Scientific,, c2019.
面頁冊數:	1 online resource (418 p.) : : ill. (some col.)
標題:	Bayesian statistical decision theory - Data processing.
標題:	Fault location (Engineering)
標題:	Neural networks (Computer science)
標題:	Electronic books.
ISBN:	9789813271494
書目註:	Includes bibliographical references and index.
摘要、提要註:	"Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases. Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system."--
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/11021#t=toc

Bayesian networks in fault diagnosis[electronic resource] :practice and application /
Bayesian networks in fault diagnosispractice and application /[electronic resource] :editors, Baoping Cai ... [et al.] - 1st ed. - Singapore :World Scientific,c2019. - 1 online resource (418 p.) :ill. (some col.)

Includes bibliographical references and index.

"Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases. Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system."--

Electronic reproduction.
Singapore :
World Scientific,
[2018]

Mode of access: World Wide Web.

ISBN: 9789813271494Subjects--Topical Terms:

558567
Bayesian statistical decision theory
--Data processing.

LC Class. No.: QA279.5 / .B39 2019

Dewey Class. No.: 519.542

Bayesian networks in fault diagnosis[electronic resource] :practice and application /
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050 0 4 $a QA279.5 $b .B39 2019
082 0 4 $a 519.542 $2 23
245 0 0 $a Bayesian networks in fault diagnosis $h [electronic resource] : $b practice and application / $c editors, Baoping Cai ... [et al.]
250 $a 1st ed.
260 $a Singapore : $b World Scientific, $c c2019.
300 $a 1 online resource (418 p.) : $b ill. (some col.)
504 $a Includes bibliographical references and index.
520 $a "Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases. Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system."-- $c Publisher's website.
533 $a Electronic reproduction. $b Singapore : $c World Scientific, $d [2018]
538 $a Mode of access: World Wide Web.
588 $a Description based on online resource; title from PDF title page (viewed August 30, 2018)
650 0 $a Bayesian statistical decision theory $x Data processing. $3 558567
650 0 $a Fault location (Engineering) $3 510431
650 0 $a Neural networks (Computer science) $3 386157
650 0 $a Electronic books. $2 local $3 376747
700 1 $a Cai, Baoping. $3 709924
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/11021#t=toc

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